penguins_plotly

glimpse(penguins)

## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex               <fct> male, female, female, NA, female, male, female, male…
## $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

summary(penguins)

##       species          island    bill_length_mm  bill_depth_mm  
##  Adelie   :152   Biscoe   :168   Min.   :32.10   Min.   :13.10  
##  Chinstrap: 68   Dream    :124   1st Qu.:39.23   1st Qu.:15.60  
##  Gentoo   :124   Torgersen: 52   Median :44.45   Median :17.30  
##                                  Mean   :43.92   Mean   :17.15  
##                                  3rd Qu.:48.50   3rd Qu.:18.70  
##                                  Max.   :59.60   Max.   :21.50  
##                                  NA's   :2       NA's   :2      
##  flipper_length_mm  body_mass_g       sex           year     
##  Min.   :172.0     Min.   :2700   female:165   Min.   :2007  
##  1st Qu.:190.0     1st Qu.:3550   male  :168   1st Qu.:2007  
##  Median :197.0     Median :4050   NA's  : 11   Median :2008  
##  Mean   :200.9     Mean   :4202                Mean   :2008  
##  3rd Qu.:213.0     3rd Qu.:4750                3rd Qu.:2009  
##  Max.   :231.0     Max.   :6300                Max.   :2009  
##  NA's   :2         NA's   :2

penguins_hm <- penguins %>%
      filter(!is.na(sex)) %>%
      count(species, island, sex) %>%
      ggplot(aes(x = sex, y = species, fill = n)) +
      geom_tile() +
      facet_grid(~ island) +
      theme_classic() +
      labs(title = "Distribution of penguins")

ggplotly(penguins_hm)

penguins_sp <- penguins %>%
      filter(!is.na(sex)) %>%
      ggplot(aes(x = bill_length_mm, y = body_mass_g, shape = sex, color = species, 
                 text = paste0("Sex: ", sex,
                               "\nIsland: ", island,
                               "\nYear: ", year))) +
      geom_point() +
      scale_shape_manual(values = c(1, 16)) +
      labs(x = "Bill length (mm)", y = "Body mass (g)")

plotly::ggplotly(penguins_sp, tooltip = "text")

penguins_sp_i <- penguins %>%
   filter(!is.na(sex)) %>%
   plotly::plot_ly(x = ~bill_length_mm, y = ~body_mass_g, color = ~species,
                type = "scatter", mode = "markers")

# install.packages("htmlwidgets")
library(htmlwidgets)
htmlwidgets::saveWidget(penguins_sp_i, "penguins_scatterplot.html")